Method and system for determining maintenance time of pipe networks of natural gas

ABSTRACT

The present disclosure provides a method and a system for determining a maintenance time of a pipe network of natural gas. The method may comprise: obtaining pipe network information of natural gas in at least one area, the pipe network information including a running time of the system and gas leakage information of the pipe network; extracting feature information based on the running time and the gas leakage information; generating a pipe network maintenance value through a maintenance value prediction model based on pipe network maintenance information and pipe network environment information, the pipe network maintenance value reflecting a priority of pipe network maintenance processing; and predicting the maintenance time of the pipe network based on the feature information and the pipe network maintenance value using a maintenance time prediction model, the maintenance time prediction model being a machine learning model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/649,190, filed on Jan. 27, 2022, which claims priority of ChinesePatent Application No. 202110154158.4, filed on Feb. 4, 2021, andChinese Patent Application No. 202210045160.2, filed on Jan. 14, 2022,the contents of each of which are entirely incorporated herein byreference.

TECHNICAL FIELD

The present disclosure relates to the field of natural gas pipe networkmanagement, and more particularly to a method and a system fordetermining a maintenance time of a pipe network of natural gas.

BACKGROUND

The pipe network of natural gas shoulders important tasks such asnatural gas transmission and energy transportation day and night, and isan indispensable and important part of natural gas transmission. Themanagement and control of pipe network life cycle of natural gas is aneffective management and control of the natural gas equipmentinfrastructure. The existing pipe network management of natural gasmainly relies on personnel self-inspection. Self-inspection is difficultto detect pipe network problems in time, and maintenance is performedbefore the pipe network is damaged, which makes the managementefficiency of the pipe network low.

Therefore, it is hoped to provide a method for determining a maintenancetime of a pipe network of natural gas by obtaining pipe networkinformation of natural gas to predict the maintenance time of pipenetwork, thereby realizing the full life cycle intelligent management ofpipe network of natural gas, prevent unnormal use because the pipenetwork of natural gas reaches a full life cycle, and avoiding seriouslosses.

SUMMARY

One of the embodiments of the present disclosure provides a method fordetermining a maintenance time of a pipe network of natural gasimplemented by a processor, the method comprising: obtaining pipenetwork information of natural gas in at least one area, the pipenetwork information including a running time of a system for determininga maintenance time of a pipe network of natural gas and gas leakageinformation of the pipe network; extracting feature information based onthe running time and the gas leakage information; generating a pipenetwork maintenance value through a maintenance value prediction modelbased on pipe network maintenance information and pipe networkenvironment information, the pipe network maintenance value reflecting apriority of pipe network maintenance processing; wherein the maintenancevalue prediction model is a Graph Neural Network model, a plurality ofnodes of the Graph Neural Network model include a plurality ofhistorical maintenance locations of the pipe network and historical pipenetwork environmental information, a plurality of edges of the GraphNeural Network model include one or more pipes between the plurality ofhistorical maintenance locations of the pipe network, features of thenodes include at least one of replacement pipe material, a maintenancetime, a maintenance location, gas leakage after maintenance, a vibrationdetection result, a vibration frequency of the pipe network, and naturalgas usage environment information, and features of the edges include atleast one of pipe material, a diameter, a connection manner, and arelationship between the historical pipe network environment informationand the historical maintenance locations of the pipe network; andpredicting the maintenance time of the pipe network based on the featureinformation and the pipe network maintenance value using a maintenancetime prediction model, the maintenance time prediction model being amachine learning model.

One of the embodiments of the present disclosure provides a system fordetermining a maintenance time of a pipe network of natural gas. Thesystem comprising: an information obtaining module configured to obtainpipe network information of natural gas in at least one area, the pipenetwork information including a running time of the system and gasleakage information of the pipe network; a feature extracting moduleconfigured to extract feature information based on the running time andthe gas leakage information; a time prediction module configured to:generate a pipe network maintenance value through a maintenance valueprediction model based on pipe network maintenance information and pipenetwork environment information, the pipe network maintenance valuereflecting a priority of pipe network maintenance processing; whereinthe maintenance value prediction model is a Graph Neural Network model,a plurality of nodes of the Graph Neural Network model include aplurality of historical maintenance locations of the pipe network andhistorical pipe network environmental information, a plurality of edgesof the Graph Neural Network model include one or more pipes between theplurality of historical maintenance locations of the pipe network,features of the nodes include at least one of replacement pipe material,a maintenance time, a maintenance location, gas leakage aftermaintenance, a vibration detection result, a vibration frequency of thepipe network, and natural gas usage environment information, andfeatures of the edges include at least one of pipe material, a diameter,a connection manner, and a relationship between the historical pipenetwork environment information and the historical maintenance locationsof the pipe network; and predict the maintenance time of the pipenetwork based on the feature information and the pipe networkmaintenance value using a maintenance time prediction model, themaintenance time prediction model being a machine learning model.

One of the embodiments of the present disclosure provides a device fordetermining a maintenance time of a pipe network of natural gas, thedevice includes at least one processor and at least one memory; the atleast one memory is used to store computer instructions; the at leastone processor is used to execute at least part of the instructions inthe computer instructions to implement the above-mentioned method formanaging a pipe network of natural gas.

One of the embodiments of the present disclosure provides anon-transitory computer readable medium storing instructions, whenexecuted by at least one processor, causing the at least one processorto implement a method comprising: obtaining pipe network information ofnatural gas in at least one area, the pipe network information includinga running time of a system for determining a maintenance time of a pipenetwork of natural gas and gas leakage information of the pipe network;extracting feature information based on the running time and the gasleakage information; generating a pipe network maintenance value througha maintenance value prediction model based on pipe network maintenanceinformation and pipe network environment information, the pipe networkmaintenance value reflecting a priority of pipe network maintenanceprocessing; wherein the maintenance value prediction model is a GraphNeural Network model, a plurality of nodes of the Graph Neural Networkmodel include a plurality of historical maintenance locations of thepipe network and historical pipe network environmental information, aplurality of edges of the Graph Neural Network model include one or morepipes between the plurality of historical maintenance locations of thepipe network, features of the nodes include at least one of replacementpipe material, a maintenance time, a maintenance location, gas leakageafter maintenance, a vibration detection result, a vibration frequencyof the pipe network, and natural gas usage environment information, andfeatures of the edges include at least one of pipe material, a diameter,a connection manner, and a relationship between the historical pipenetwork environment information and the historical maintenance locationsof the pipe network; and predicting the maintenance time of the pipenetwork based on the feature information and the pipe networkmaintenance value using a maintenance time prediction model, themaintenance time prediction model being a machine learning model.

One of the embodiments of the present disclosure provides acomprehensive management method and system for the life cycle of anatural gas energy metering Internet of Things. By receiving targetsystem uptime information, the target system uptime information carriesthe predicted running time difference corresponding to the historicalrunning time information and predicted damage period information;extracting time features based on the predicted running time differenceand the predicted damage period information, and determining the actualrunning time difference corresponding to the predicted running timedifference and the predicted damage period information for providingrunning services value and actual damage cycle information; sending thetarget system normal running time information to the actual running timedifference value and the actual damage period information, so as tomanage the system actual running time according to the target systemnormal running time information.

One of the embodiments of the present disclosure provides acomprehensive management method for the life cycle of a natural gasenergy metering Internet of Things system, the method furthercomprising: before receiving the target system uptime information, thecoefficient corresponding to the actual operating time of the receivingsystem; wherein, the coefficient value of the actual running time of thesystem is carried in the coefficient corresponding to the actual runningtime of the system; according to described coefficient value, assigndescribed predicted running time difference value and describedpredicted damage period information to described system actual runningtime; according to described coefficient value, determine describedactual running time difference value and described actual damage cycleinformation; adding the mapping relationship between the predictedrunning time difference, the predicted damage period information and theactual running time difference, the actual damage period informationinto the time feature; according to described coefficient value anddescribed actual operation time difference value, described actualdamage cycle information distribution described system actual runningtime and system actual running time mark.

One of the embodiments of the present disclosure provides acomprehensive management method for the life cycle of a natural gasenergy metering Internet of Things system, the method furthercomprising: according to the coefficient value, assign the historicalrunning time average value corresponding to the predicted running timedifference value to the actual running time of the system; wherein thedetermining the actual running time difference value and the actualdamage period information according to the coefficient value includes;for each actual damage period information, determine an actual runningtime difference and an actual damage period information according to thecoefficient value; wherein the mapping relationship between thepredicted running time difference, the predicted damage periodinformation, the actual running time difference, and the actual damageperiod information is added to the time features include: adding themapping relationship between the predicted damage period information andthe historical running time average value into the time feature; addingthe mapping relationship between the predicted running time difference,each actual damage period information, the actual running timedifference corresponding to each actual damage period information, andthe actual damage period information to the time feature.

One of the embodiments of the present disclosure provides a kind ofnatural gas energy metering Internet of Things system life cyclecomprehensive management method, described according to the predictedrunning time difference value and predicted damage period informationextraction time feature, determine the predicted running time differenceThe actual operating time difference and the actual damage periodinformation used to provide the operating service corresponding to thevalue and the predicted damage period information, including: extractingtime features from the predicted damage period information, anddetermining the actual running time difference value and actual damageperiod information corresponding to the predicted running timedifference value and the predicted damage period information forproviding the operation service, the specific operations may include:according to the mapping relationship between the predicted damageperiod information and the historical running time average value,determining the historical running time average value corresponding tothe predicted damage period information; according to the historicalrunning time average value, extracting time feature respectively,obtaining the actual running time difference and the actual damageperiod information corresponding to each actual damage periodinformation.

One of the embodiments of the present disclosure provides acomprehensive management method for the life cycle of a natural gasenergy metering Internet of Things system, the first running timecalculation model is also carried in the coefficient corresponding tothe actual running time of the system, and the described coefficientvalue is based on assigning the predicted run time difference and thepredicted damage period information to the system actual run time,including: according to the coefficient value, the first running timecalculation model to allocate the predicted running time differencevalue and the predicted damage period information for the actual runningtime of the system; determining the actual running time difference valueand the actual damage period information according to the coefficientvalue, including: according to the coefficient value, the first runningtime calculation model to determine the actual running time differencevalue and the actual damage cycle information; adding the mappingrelationship between the predicted running time difference, thepredicted damage period information and the actual running timedifference, and the actual damage period information into the timefeature include; the mapping relationship between the predicted runningtime difference, the predicted damage period information and the actualrunning time difference, the actual damage period information, and thefirst running time calculation model add to the temporal feature.

One of the embodiments of the present disclosure provides acomprehensive management method for the life cycle of a natural gasenergy metering Internet of Things system, the method further comprises;receiving replacement system time information, and described replacementsystem time information carries the second running time calculationmodel and system actual running time mark; determining the coefficientvalue of the actual running time of the system according to the actualrunning time of the system; according to the coefficient value and thesecond running time calculation model, assigning the actual damageperiod information and the actual damage parameter to the actual runningtime of the system; determining the replacement time error coefficientvalue corresponding to the actual running time difference valueaccording to the coefficient value and the second running timecalculation model; adding the mapping relationship between the actualdamage period information, the actual damage parameter and the actualrunning time difference, the replacement time error coefficient value,and the second running time calculation model to the time feature.

One of the embodiments of the present disclosure provides acomprehensive management method for the life cycle of a natural gasenergy metering Internet of Things system, the method further comprises,monitoring the state of the actual system running time corresponding tothe system actual running time mark; when determining that the validperiod corresponding to the actual running time of the system arrives,release the actual running time of the system; removing the mappingrelationship described in the time feature.

One of the embodiments of the present disclosure provides acomprehensive management method for the life cycle of a natural gasenergy metering Internet of Things system, the target system normalrunning time information also carries user identification, in accordingto described predicted running time difference value and describedpredicted damage period information extraction time feature, determiningdescribed predicted running time difference value and all before theactual running time difference and the actual damage period informationcorresponding to the predicted damage period information for providingthe running service.

One of the embodiments of the present disclosure provides acomprehensive management method for the life cycle of a natural gasenergy metering Internet of Things system, and the target system uptimeinformation is a request for writing data, and the request for writingdata further includes data to be written, the sending the target systemuptime information to the actual running time difference value and theactual damage period information includes: Sending the data to bewritten to the actual operating time difference value and the actualdamage period information to replace the data corresponding to theactual operating time identification of the system.

One of the embodiments of the present disclosure provides a life cycleintegrated management system of a natural gas energy metering Internetof Things system, and the system includes: a user platform, a serviceplatform, a management platform, a sensor network platform and aperception control platform. The user platform is communicativelyconnected to the service platform, the service platform iscommunicatively connected to the management platform, the managementplatform is communicatively connected to the sensor network platform,and the sensor network platform is communicatively connected to theperception control platform. The management platform further includes adata acquisition terminal and a data processing terminal, the dataacquisition terminal and the data processing terminal are connected incommunication, and the data processing terminal is specifically usedfor: receiving target system uptime information, the target systemuptime information carries the predicted running time differencecorresponding to the historical running time information and predicteddamage period information; extracting time features based on thepredicted running time difference and the predicted damage periodinformation, and determining the actual running time differencecorresponding to the predicted running time difference and the predicteddamage period information for providing running services value andactual damage cycle information; sending the target system normalrunning time information to the actual running time difference value andthe actual damage period information, so as to manage the system actualrunning time according to the target system normal running timeinformation.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be further described in the form ofexemplary embodiments, and these exemplary embodiments will be describedin detail with the drawings. These embodiments are not restrictive. Inthese embodiments, the same number represents the same structure, inwhich:

FIG. 1 illustrates an application scenario of a system for managing apipe network of natural gas according to some embodiments of the presentdisclosure;

FIG. 2 illustrates an exemplary flow diagram of a method for managing apipe network of natural gas according to some embodiments of the presentdisclosure;

FIG. 3 illustrates an exemplary schematic diagram of a method forpredicting maintenance time according to other embodiments of thepresent disclosure;

FIG. 4 illustrates an exemplary schematic diagram of a method forpredicting a maintenance value according to some embodiments of thepresent disclosure;

FIG. 5 illustrates an exemplary module diagram of a system for managinga pipe network of natural gas according to some embodiments of thepresent disclosure;

FIG. 6 illustrates another schematic diagram of a system for managing apipe network of natural gas according to some embodiments of the presentdisclosure;

FIG. 7 illustrates another exemplary flow diagram of a method formanaging a pipe network of natural gas according to some embodiments ofthe present disclosure;

FIG. 8 illustrates a module diagram of a device for managing a pipenetwork of natural gas according to some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

In order to more clearly illustrate the technical solutions of theembodiments of the present disclosure, the following will brieflyintroduce the drawings that need to be used in the description of theembodiments. Obviously, the drawings in the following description areonly some examples or embodiments of the disclosure. For those ofordinary skill in the art, without creative work, the disclosure can beapplied to the application other similar scenarios according to thesedrawings. Unless it is obvious from the language environment orotherwise stated, the same reference numbers in the drawings representthe same structure or operation.

It should be understood that the “system”, “device”, “unit” and/or“module” used herein is a method for distinguishing differentcomponents, elements, parts, parts, or assemblies of different levels.However, if other words can achieve the same purpose, the words can bereplaced by other expressions.

As shown in the present disclosure and the claims, unless the contextclearly suggests exceptional circumstances, the words “a”, “an”, “an”and/or “the” do not specifically refer to the singular, but may alsoinclude the plural. Generally speaking, the terms “including” and“contain” only suggest that the operations and elements that have beenclearly identified are included, and these operations and elements donot constitute an exclusive list, and the method or device may alsoinclude other operations or elements.

Flow diagram s are used in the present disclosure to illustrate theoperations performed by the system according to the embodiments of thepresent disclosure. It should be understood that the preceding orfollowing operations are not necessarily performed precisely in order.Instead, the individual operations can be processed in reverse order orsimultaneously. At the same time, users can also add other operations tothese processes, or remove an operation or several operations from theseprocesses.

FIG. 1 illustrates an application scenario of a system 100 for managinga pipe network of natural gas according to some embodiments of thepresent disclosure. As shown in the application scenario, the system 100may include a server 110, a processor 112, a storage device 120, a userterminal 130, a network 140, and a pipe network 150 of natural gas.

The system for managing a pipe network of natural gas may be used forthe service platform for managing pipe network of natural gas, mayrealize the purpose of managing pipe network of natural gas byimplementing the method and/or process disclosed in the presentdisclosure.

The server 110 may communicate with the processor 112, the storagedevice 120, and the user terminal 130 through the network 140 to providevarious functions of managing pipe network of natural gas, and thestorage device 120 may store all the information of the natural gas pipenetwork operation process. In some embodiments, the user terminal 130may send a natural gas pipe network information acquisition request tothe server 110 and receive feedback information from the server 110. Theinformation transfer relationship between the above devices is only anexample, and the present disclosure is not limited.

In some embodiments, the server 110, the user terminal 130, and otherpossible components may include the storage device 120.

In some embodiments, the server 110, the user terminal 130, and otherpossible components may include the processor 112.

The server 110 may be used to manage resources and process data and/orinformation from at least one component of the present system or anexternal data source (e.g., a cloud data center). In some embodiments,server 110 may be a single server or a group of servers. The servergroup may be centralized or distributed (e.g., the server 110 may be adistributed system), dedicated or concurrently provided by other devicesor systems. In some embodiments, the server 110 may be regional orremote. In some embodiments, server 110 may be implemented on a cloudplatform, or provided in a virtual fashion.

The processor 112 may process data and/or information obtained fromother devices or system components. The processor may execute programinstructions based on such data, information and/or processing resultsto perform one or more of the functions described herein. In someembodiments, the processor 112 may include one or more sub-processingdevices (e.g., single-core processing devices or multi-core processingdevices). In some embodiments, the processor 112 may be integrated withthe server 110.

The storage device 120 may be used to store data and/or instructions.The storage device 120 may include one or more storage components, andeach storage component may be an independent device or a part of otherdevices. In some embodiments, storage device 120 may include a randomaccess memory (RAM), a read only memory (ROM), a mass memory, aremovable memory, a volatile read-write memory, etc., or any combinationthereof. Illustratively, mass memory may include magnetic disks, opticaldisks, solid state disks, or the like. In some embodiments, the storagedevice 120 may be implemented on a cloud platform.

The user terminal 130 refers to one or more terminal devices or softwareused by the user. In some embodiments, one or more users may use theuser terminal 130, which may include users who directly use the service,and may also include other related users. In some embodiments, userterminal 130 may be one of mobile device 130-1, tablet computer 130-2,laptop computer 130-3, desktop computer 130-4, etc., and other deviceswith input and/or output capabilities species or any combinationthereof.

The above examples are only used to illustrate the breadth of the scopeof the user terminal 130 equipment rather than limit its scope.

The network 140 may connect components of the system and/or connect thesystem with external resource portions. The network 140 enablescommunication between the various components and with other componentsoutside the system, facilitating the exchange of data and/orinformation. In some embodiments, the network 140 may be any one or moreof a wired network or a wireless network. In some embodiments, thenetwork may be in point-to-point, shared, centralized, etc. varioustopologies or a combination of a plurality of topologies. In someembodiments, network 140 may include one or more network access points.For example, network 140 may include wired or wireless network accesspoints through which one or more components of the natural gas networkmanagement system may be connected to network 140 to exchange dataand/or information.

The pipe network 150 of natural gas refers to a network of pipe thattransport natural gas from extraction sites or processing plants tourban gas distribution centers or users. In some embodiments, the pipenetwork 150 of natural gas may include gas gathering pipes, gastransmission pipes, gas distribution pipes, or the like. The gasgathering pipe refers to the pipe from the gas field wellhead devicethrough the gas gathering station to the gas treatment plant or thestarting gas compressor station, which is used to collect the untreatednatural gas extracted from the formation. The gas pipe refers to thepipe from the gas processing plant or the starting point compressorstation of the gas source to the gas distribution centers, large usersor gas storages in major cities, and the pipes that communicate witheach other between the gas sources. It is used to transport natural gasthat has been processed to meet pipeline quality standards. The gasdistribution pipe refers to the pipe from the urban pressure regulatingand measuring station to the user branch line. In some embodiments, thepipe network 150 of natural gas may be assembled from individual pipesconnected one by one.

It should be noted that the above descriptions of the applicationscenarios of the system for managing pipe network of natural gas areonly for convenience of description, and cannot limit the presentdisclosure to the scope of the illustrated embodiments. It can beunderstood that for those skilled in the art, after understanding theprinciple of the system, it is possible to arbitrarily combine variouscomponents, or form subcomponents to connect with other componentswithout departing from this principle. In some embodiments, the server,the processor, and the memory disclosed in FIG. 1 may be different unitsin one component, or may be one component that implements the functionsof the above-mentioned two or more components. For example, eachcomponent may share one storage unit, and each component may also haveits own storage unit. Such deformations are within the scope ofprotection of this manual.

FIG. 2 illustrates an exemplary flow diagram of a method for managing apipe network of natural gas according to some embodiments of the presentdisclosure. As shown in FIG. 2 , the process 200 may include thefollowing operations. In some embodiments, process 200 may be executedby processor 112.

In operation 210, pipe network information of natural gas in at leastone area may be obtained. In some embodiments, the information obtainingmodule 510 executes operation 210.

Among then, the area may be a district, a school, a city, or a certainspecific area delineated, which may be determined according to actualmanagement needs.

In some embodiments, the pipe network information of natural gas mayinclude a running time of a system of the pipe network of the naturalgas and gas leakage information of the pipe network.

The running time of the system refers to a time from a time point whenthe system for managing the pipe network of natural gas starts (whichmay include the first start or maintenance restart, etc.) to a timepoint when running time information of the system is collected, or atime from a time point when the system has been running since the end ofthe last maintenance to a time point when the running time informationwas collected. For example, if the system for managing the pipe networkof natural gas was operated for the first time on Jan. 1, 2021, and hasnot been maintained until today, Feb. 1, 2021, the running time of thesystem is 31 days. For another example, if the system for managing thepipe network of natural gas was last maintained and restarted on Mar. 1,2021, and it has not been maintained until Apr. 1, 2021, the runningtime of the system is 31 days.

The gas leakage information of the pipe network refers to informationrelated to natural gas leakage. In some embodiments, the gas leakageinformation of the pipe network may include information such as a gasconcentration, a diffusion rate, a leakage time, an elapsed time, and agas leakage location of the leaked natural gas.

In some embodiments, the processor 112 may obtain the running time ofthe system based on historical information. The historical informationmay refer to various types of information collected and recorded in thepast, including the running time of the system. In some embodiments, theprocessor 112 may obtain the gas concentration, diffusion rate, leakagetime, and elapsed time of the leaked natural gas through the gasdetector. In some embodiments, the processor 112 may obtain a gasleakage location of the leaked natural gas through the detection recordof the leakage information. In some embodiments, the processor 112 mayintegrate information such as gas concentration, diffusion rate, leakagetime, elapsed time, and gas leakage location of the leaked natural gas,so as to obtain the gas leakage information of the pipe network.

In operation 220, feature information based on the running time and thegas leakage information may be extracted. In some embodiments, thefeature extracting module 520 executes operation 220.

The feature information may be an abstract expression obtained by meansof feature extraction for the features of the system's running time andthe gas leakage information. In some embodiments, the featureinformation may be in the form of feature vectors or matrices.

In some embodiments, feature information may be obtained by conversionalgorithm, model processing, etc.

In some embodiments, the running time and the leakage information of thepipe network may be quantized to obtain the feature information.Specifically, a conversion algorithm may be used to determine the vectorcorresponding to the running time and the leakage information of thepipe network. The conversion algorithm may include a one-hot encodingalgorithm, a collinear vector algorithm, a Glove algorithm, or the like.Exemplarily, the one-hot encoding algorithm may be used to convert therunning time and the leakage information of the pipe network into avector representation. The one-hot encoding, also known as one-bit validencoding or one-hot encoding, mainly uses N-bit state registers toencode N states, each state has an independent register bit, and onlyone bit is valid at any time. The one-hot encoding is the representationof categorical variables as binary vectors. For example, assuming thatthe tag type includes running time and gas leakage information,according to the principle of encoding N states by the N-bit stateregister, where N=2, after encoding: the tag corresponding to therunning time can be expressed as [1, 0], the label corresponding to thegas leakage information can be expressed as [0, 1].

In some embodiments, the vector corresponding to the running time andthe gas leakage information of the pipe network may be spliced orsuperimposed to obtain an eigenvector or a matrix.

In some embodiments, the first model may be used to execute the runningtime and the gas leakage information of the pipe network to obtainfeature information. For example, the running time and the gas leakageinformation of the pipe network may be input into the first model, andthe feature information may be output from the first model. The firstmodel may be a Word2Vec model, a BERT model, a CNN model, a DNN model,or the like. The first model may be trained by using the historicalrunning time and the historical gas leakage information as trainingdata, so that the first model may output its corresponding vector ormatrix representation based on the running time and the gas leakageinformation. Labels corresponding to training data may be determined bytransformation algorithms, human input, or historical data.

In operation 230, predicting a maintenance time of the pipe network byinputting the feature information into a maintenance time predictionmodel. In some embodiments, predicting module 530 executes operation230.

The predicted maintenance time of the pipe network refers to the timepoint at which the pipe network is maintained and obtained through theprediction of the maintenance time prediction model. For example, todayis Jan. 1, 2022, and the maintenance time of the pipe network obtainedthrough the prediction of the maintenance time prediction model may beJun. 30, 2022.

In some embodiments, after the feature information is input into themaintenance time prediction model, the maintenance time prediction modelcan output the predicted maintenance time of the pipe network.

In some embodiments, the maintenance time prediction model may beconstructed based on a deep learning neural network model. Exemplarydeep learning neural network models may include convolutional networkmodels (CNN), fully convolutional neural network (FCN) models,generative adversarial networks (GAN), backpropagation (BP) machinelearning models, radial basis functions (RBF) Machine Learning Model,Deep Belief Network (DBN), Elman Machine Learning Model, etc. or acombination thereof.

For more content about the maintenance time prediction model, refer toother parts of the present disclosure (for example, FIG. 3 and itsrelated description).

By predicting the maintenance time of the pipe network, maintenance canbe performed before the pipe network of natural gas is damaged, so as toprevent the underground pipe network from being unable to be usednormally due to the full life cycle, thereby avoiding serious losses. Inaddition, the running time and the gas leakage information of the pipenetwork are used as the input of the pipe network maintenance timeprediction model, and the accuracy of the prediction of the pipe networkmaintenance time may be improved by combining the running time and thegas leakage information.

FIG. 3 illustrates an exemplary schematic diagram of a method forpredicting maintenance time according to other embodiments of thepresent disclosure. As shown in FIG. 3 , the method 300 includes arunning time 310-1, gas leakage information of the pipe network 310-2,feature information 320-1, a pipe network maintenance value 320-2, avibration fatigue factor of the pipe network 320-3, a maintenance timeprediction model 330 and the maintenance time of the pipe network 340,and also include the initial maintenance time prediction model 350 andthe first training sample 360.

In some embodiments, as shown in FIG. 3 , the input to the maintenancetime prediction model 330 may be feature information 320-1. The featureinformation 320-1 may be obtained based on running time 310-1 and gasleakage information 310-2. For more information about the extraction ofthe feature information 320-1, refer to other parts of the presentdisclosure (for example, FIG. 2 and its related descriptions).

In some embodiments, the input of the maintenance time prediction model330 may also include a pipe network maintenance value 320-2. The dashedboxes in FIG. 3 indicate optional.

The pipe network maintenance value may be a numerical value or a letteror the like that can reflect the maintenance processing priority. Forexample, the pipe network maintenance value may be represented by anumerical value between 1-10, or the letters a-f, or a star rating. Thelarger the value, the larger the dictionary order, or the higher thestar rating, the higher the priority of maintenance processing.

In some embodiments, the pipe network maintenance value may be obtainedbased on pipe network maintenance information and pipe networkenvironment information. In some embodiments, the pipe networkmaintenance value may be obtained through a maintenance value predictionmodel. For more information about the acquisition of the pipe networkmaintenance value, refer to other parts of the present disclosure (forexample, FIG. 4 and its related descriptions).

In some embodiments, the input of the maintenance time prediction model330 may also include a fatigue factor of the network vibration 320-3.

The vibration fatigue factor of the pipe network may be used to reflectthe magnitude of the strength of the vibration fatigue of the pipe dueto vibration. For example, the vibration factor of the pipe network maybe represented by a value between 1 and 10. The larger the value, thegreater the fatigue strength of the pipe due to vibration.

In some embodiments, the vibration fatigue factor of the pipe networkmay be obtained based on a vibration frequency of the pipe network and avibration time of the pipe network. In some embodiments, the vibrationfatigue factor of the pipe network may be directly calculated based onthe vibration frequency of the pipe network and the vibration time ofthe pipe network. In some embodiments, the vibration fatigue factor ofthe pipe network may be obtained by inputting the vibration frequency ofthe pipe network and the vibration time of the pipe network into thesecond model, and outputting the vibration fatigue factor of the pipenetwork from the second model.

In some embodiments, the processor 112 may detect the vibration time ofthe pipe network through the sensor. For example, the processor 112 maydetect through the sensor that the pipe network vibrates at a certaintime period of a certain day, and determine the vibration time of thepipe network. In some embodiments, the processor 112 may obtain thevibration frequency of the pipe network through periodic detection bythe sensor. For more information about the vibration frequency of thepipe network, refer to other parts of the present disclosure (forexample, FIG. 4 and its related descriptions).

In some embodiments, the process of calculating the vibration fatiguefactor of the pipe network may be expressed as: the vibration fatiguefactor of the pipe network=the vibration frequency of the pipenetwork*the vibration time of the pipe network/pipe material strength.

Among then, the larger the vibration frequency of the pipe network andthe vibration time of the pipe network are, the easier the pipe is toproduce fatigue, and the larger the corresponding the vibration fatiguefactor of the pipe network. By obtaining the vibration frequency of thepipe network and the vibration time of the pipe network, the foundationmay be laid for the subsequent accurate calculation of the vibrationfatigue factor of the pipe network. The pipe material strength is theability of the pipe material to resist damage under the action ofexternal force. In some embodiments, the pipe material strength mayinclude tensile strength, compressive strength, shear strength, flexuralstrength, or the like. The smaller the pipe material strength, theeasier the pipe is fatigued, and the greater the corresponding vibrationfatigue factor of the pipe network. By setting the pipe materialstrength, the influence of different materials on the ease of fatigue ofthe pipe may be comprehensively considered, so as to improve theaccuracy of determining the vibration fatigue factor of the pipenetwork.

In some embodiments, the vibration frequency of the pipe network and thevibration time of the pipe network may be input into the second model toobtain the vibration fatigue factor of the pipe network. The secondmodel may be a CNN model, a DNN model, or the like. The second model maybe trained by using historical vibration frequency of the pipe networkand historical vibration time of the pipe network as training data, sothat the second model may output vibration fatigue factor of the pipenetwork based on the vibration frequency of the pipe network and thevibration time of the pipe network. The labels of the training data maybe manually-labeled vibration fatigue factors of the pipe network.

In some embodiments, the input of the maintenance time prediction model330 may be the combination of feature information, the pipe networkmaintenance value, and the vibration fatigue factor of the pipe network,and the output of the maintenance time prediction model 330 is themaintenance time of the pipe network 340.

In some embodiments, as shown in FIG. 3 , the model parameters of themaintenance time prediction model 330 may be obtained by training aplurality of first training samples 360 with labels. In someembodiments, the plurality of sets of first training samples 360 may beobtained based on historical data, and each set of first trainingsamples 360 may include a plurality of training data and labelscorresponding to the training data. Taking the input of the maintenancetime prediction model 330 as the feature information 320-1 as anexample, the training data may include feature information correspondingto historical running time and historical gas leakage information, andthe label of the training data may be the manually-marked maintenancetime of the pipe network. Taking the input of the maintenance timeprediction model 330 as the feature information 320-1 and the pipenetwork maintenance value 320-2 as an example, the training data mayinclude the feature information corresponding to the historical runningtime and the historical gas leakage information, and the featureinformation based on the historical pipe network maintenance informationand the maintenance value of the pipe network obtained from thehistorical pipe network environment information, and the label of thetraining data may be the maintenance time of the pipe network markedmanually. Taking the input of the maintenance time prediction model 330as the feature information 320-1, the maintenance value of the pipenetwork 320-2, and the vibration fatigue factor of the pipe network320-3 as examples, the training data may include the feature informationcorresponding to the historical running time and the historical gasleakage information, the historical maintenance value of the pipenetwork, and the vibration fatigue factor of the pipe network calculatedbased on the historical vibration frequency of the pipe network andhistorical vibration time of the pipe network. The label of the trainingdata may be the manually-marked maintenance time of the pipe network.

The parameters of the initial maintenance time prediction model 350 maybe updated through a plurality of groups of first training samples 360to obtain the trained initial maintenance time prediction model 350.Among them parameters can be passed in any common way.

In some embodiments, the parameters of the initial maintenance timeprediction model 350 may be iteratively updated based on a plurality offirst training samples, so that the loss function of the model satisfiesa preset condition. For example, the loss function converges, or theloss function value is smaller than a preset value. When the lossfunction satisfies the preset condition, the model training iscompleted, and the trained initial maintenance time prediction model 350is obtained. The maintenance time prediction model 330 and the trainedinitial maintenance time prediction model 350 have the same modelstructure.

The maintenance time of the pipe network is predicted by the maintenancetime prediction model, and maintenance may be carried out before thepipe network of natural gas is damaged, so as to prevent the undergroundpipe network from being unable to be used normally due to the full lifecycle, thereby avoiding serious losses. The maintenance value of thepipe network or the vibration fatigue factor of the pipe network may beused as the input of the maintenance time prediction model to obtain aprediction result associating the feature information obtained based onthe running time and gas leakage information with the maintenance valueof the pipe network, or the feature information obtained based on therunning time and gas leakage information with the vibration fatiguefactor. It makes the maintenance time prediction model more accurate inpredicting the maintenance time of the pipe network.

It should be noted that the above description about the method forpredicting the process maintenance time is only for example andillustration, and does not limit the scope of application of the presentdisclosure. For those skilled in the art, various modifications andchanges can be made to the process maintenance time prediction methodunder the guidance of the present disclosure. However, these correctionsand changes are still within the scope of this present disclosure. Forexample, the input of the second model may also include pipe materialstrength. The vibration frequency of the pipe network, vibration time ofthe pipe network, and pipe material strength may be input into thesecond model to obtain the vibration fatigue factor of the pipe network.The second model may be a CNN model, a DNN model, or the like. Thesecond model may be trained by using historical vibration frequency ofthe pipe network, historical vibration time of the pipe network andhistorical pipe material strength as training data, so that the secondmodel may output the vibration fatigue factor of the pipe network basedon the vibration frequency of the pipe network, the vibration time ofthe pipe network and pipe material strength. The labels of the trainingdata may be manually labeled as the vibration fatigue factor of the pipenetwork.

FIG. 4 illustrates an exemplary schematic diagram of a method forpredicting maintenance value according to some embodiments of thepresent disclosure. As shown in FIG. 4 , the method 400 includes pipenetwork maintenance information 410, pipe network environmentinformation 420, a maintenance value prediction model 430 and a pipenetwork maintenance value 440, and also includes an initial maintenancevalue prediction model 450 and a second training sample 460.

A maintenance value prediction model may be used to predict amaintenance value of the pipe network. In some embodiments, themaintenance value of the pipe network may be obtained based on the pipenetwork maintenance information and the pipe network environmentinformation through a maintenance value prediction model.

In some embodiments, the maintenance value prediction model may be aConvolutional Neural Network (CNN), a Fully Convolutional neural Network(FCN) model, a Generative Adversarial Network (GAN), a Back Propagation(BP) machine learning model, a Radial Basis Function machine learningmodel (RBF), a Deep Belief Network (DBN), an Elman Machine learningmodel (EM), etc. or a combination thereof.

In some embodiments, the maintenance value prediction model may be aGraph Neural Network (GNN) model. The nodes of the GNN model are thehistorical maintenance locations of the pipe network and pipe networkenvironment information, and the edges are pipes between the historicalmaintenance locations of the pipe network.

In some embodiments, as shown in FIG. 4 , the input of the maintenancevalue prediction model 430 may include the pipe network maintenanceinformation 410 and the pipe network environment information 420, andthe output is the maintenance value of the pipe network 440. In someembodiments, the input to the maintenance value prediction model 430 mayinclude historical data over a certain period of time in the past (e.g.,last a month, last 2 months, last 3 months, etc.).

In some embodiments, the pipe network maintenance information mayinclude at least one of replacement pipes, a maintenance time, specificlocations for maintenance (e.g., connections, elbows, branch pipes,etc.), gas leakage after maintenance, and vibration detection results.In some embodiments, the processor 112 may obtain pipe networkmaintenance information based on historical maintenance records.

In some embodiments, the pipe network environment information mayinclude the vibration frequency of the pipe network and natural gasusage environment information.

In some embodiments, the natural gas usage environment information mayinclude an average ventilation rate of natural gas within a cell. Theprocessor 112 may obtain natural gas usage environment informationthrough sensor detection.

In some embodiments, the vibration frequency of the pipe network mayinclude a natural frequency of a pipe and an external vibrationfrequency. The natural frequency is the frequency of vibration generateddue to changes in the elbow, diameter, etc. of the pipe, and due to theflow of natural gas. The external vibration frequency may be thefrequency of vibration caused by the surrounding construction site,traffic, an unstable pipe support, etc.

In some embodiments, the processor 112 may obtain the vibrationfrequency of the pipe network by periodically detecting the sensor. Thedetected historical data may be stored in the storage device 120. Whenpredicting the maintenance value of the pipe network through themaintenance value prediction model, the data in a certain period of timein the past (for example, 1 week, 1 month, 2 months, 3 months, etc.) isselected as the model input.

In some embodiments, the length of the corresponding time period of theselection data is negatively correlated with the maintenance value ofthe pipe network within a certain range. For example, if the maintenancevalue of the pipe network is large, the priority of maintenanceprocessing is high, and the selection of the time period may berelatively small, in order to speed up the prediction or avoid theinterference of irrelevant data. For another example, the maintenancevalue of the pipe network is small, the priority of maintenanceprocessing is low, and the selection of the time period may berelatively large to ensure the prediction effect.

In some embodiments, the GNN model may process graph data constructedbased on the relationship between the maintenance historical pipenetwork locations and historical pipe network environmental informationto determine maintenance values of the pipe network. In someembodiments, the graph may include a plurality of nodes and a pluralityof edges, where nodes correspond to the historical maintenance locationsof the pipe network and pipe network environment information, and edgescorrespond to pipes between the historical maintenance locations. Insome embodiments, the edge corresponds to the spatial positionrelationship between the historical pipe network maintenance locationsand the pipe network environment information, and the spatial positionrelationship may be a relative position relationship, a distancerelationship, or the like. In some embodiments, nodes and edges eachcontain their own features. In some embodiments, the features of thenodes may include replacement pipes, maintenance time, specificmaintenance locations, gas leakage after maintenance, vibrationdetection results, vibration frequencies of the pipe network, andnatural gas usage environment information, or the like. The feature ofeach edge may include the pipe material, diameter, connection method,and the relationship between the pipe network environment informationand the pipe network maintenance location (for example, thecorrespondence between a maintenance point and a pipe networkenvironment information).

In some embodiments, as shown in FIG. 4 , the output of the maintenancevalue prediction model 430 is the maintenance value of the pipe network440.

In some embodiments, as shown in FIG. 4 , the parameters of themaintenance value prediction model 430 may be obtained by training aplurality of labeled second training samples 460. In some embodiments, aplurality of sets of second training samples 460 may be obtained, eachset of second training samples 460 may include a plurality of trainingdata and labels corresponding to the training data, and the trainingdata may include historical pipe network maintenance information andhistorical pipe network environment Information, among which, thehistorical pipe network maintenance information and historical pipenetwork environment information are the pipe network maintenanceinformation and pipe network environment information in the historicaltime period, and the label of the training data may be the pipe networkmaintenance value directly marked manually according to the maintenancerecord.

The parameters of the initial maintenance value prediction model 450 maybe updated by a plurality of groups of second training samples 460, andthe trained maintenance value prediction model 450 is obtained.

In some embodiments, the parameters of the initial maintenance valueprediction model 450 may be iteratively updated based on a plurality ofsecond training samples, so that the loss function of the modelsatisfies a preset condition. For example, the loss function converges,or the loss function value is smaller than a preset value. When the lossfunction satisfies the preset condition, the model training iscompleted, and the trained initial maintenance value prediction model450 is obtained. The maintenance value prediction model 430 and thetrained initial maintenance value prediction model 450 have the samemodel structure.

By the maintenance value prediction model, the maintenance value of thepipe network is predicted, the maintenance value of the pipe network maybe used as the input of the maintenance time prediction model, to obtainthe feature information obtained based on the running time and theleakage information and the maintenance value of the pipe network.Interrelated prediction results make the maintenance time predictionmodel more accurate in predicting the maintenance time of the pipenetwork.

FIG. 5 illustrates an exemplary module diagram of a system for managinga pipe network of natural gas according to some embodiments of thepresent disclosure. As shown in FIG. 5 , the system for managing pipenetwork of natural gas 500 may at least include an information obtainingmodule 510, a feature extracting module 520 and a predicting module 530.

The information obtaining module 510 may be used to obtain pipe networkinformation of natural gas in at least one area, the pipe networkinformation including a running time of a system of the pipe network ofthe natural gas and gas leakage information of the pipe network. Amongthem, for more details about the natural gas pipe network information,refer to FIG. 2 and its related descriptions.

The feature extracting module 520 may be used to extract featureinformation based on running time and the gas leakage information. Amongthem, for more details about the feature information, refer to FIG. 2and its related descriptions.

The time predicting module 530 may be used to predict a maintenance timeof the pipe network by inputting the feature information into amaintenance time prediction model. In some embodiments, the input of themaintenance time prediction model of the time prediction module 530further includes a pipe network maintenance value; the pipe networkmaintenance value is obtained through the maintenance value predictionmodel based on the pipe network maintenance information and the pipenetwork environment information. In some embodiments, the pipe networkenvironment information of the time prediction module 530 includes thevibration frequency of the pipe network and the natural gas usageenvironment information. The pipe network maintenance informationcomprises at least one of a replacement pipe material, a maintenancetime, a specific location of the maintenance, a gas leakage after themaintenance or a result of vibration detection. In some embodiments, theinput of the maintenance time prediction model of the time predictionmodule 530 further includes a vibration fatigue factor of the pipenetwork, and the vibration fatigue factor of the pipe network iscalculated based on the vibration frequency of the pipe network and thevibration time of the pipe network. For more details on the maintenancetime prediction model, refer to FIG. 3 and its related description.

It should be noted that the above description of the system and itsmodules is only for the convenience of description, and cannot limit thepresent disclosure to the scope of the illustrated embodiments. It canbe understood that for those skilled in the art, after understanding theprinciple of the system, various modules may be combined arbitrarily, ora subsystem may be formed to connect with other modules withoutdeparting from the principle. In some embodiments, the informationobtaining module 510, the feature extracting module 520 and thepredicting module 530 disclosed in FIG. 1 may be different modules in asystem, or may be a module that implements the functions of the abovetwo or more modules. For example, each module may share one storagemodule, and each module may also have its own storage module. Suchdeformations are within the scope of protection of the presentdisclosure.

FIG. 6 is another schematic diagram of a natural gas pipe networkmanagement system according to some embodiments of the presentdisclosure. As shown in FIG. 6 , the system for managing pipe network ofnatural gas 600 may include a data obtaining terminal 610 and a dataprocessing terminal 620, and the data obtaining terminal 610 isconnected in communication with the data processing terminal 620.

In some embodiments, the data processing terminal 620 may be a desktopcomputer, a tablet computer, a notebook computer, a mobile phone, orother data obtaining terminals that can realize data processing and datacommunication, which is not limited.

FIG. 7 illustrates another exemplary flow diagram of a method formanaging a pipe network of natural gas according to some embodiments ofthe present disclosure. As shown in FIG. 7 , the method for managingpipe network of natural gas may be applied to the data processingterminal 620 in FIG. 6 . Further, the method for managing pipe networkof natural gas 700 may include the content described in the followingoperation 710 to operation 730.

In operation 710, receiving target system uptime information, and thetarget system uptime information carries the predicted operating timedifference and predicted damage period information corresponding to thehistorical running time information.

In operation 720, extracting time features based on the predictedrunning time difference and the predicted damage period information, anddetermining the actual running time difference corresponding to thepredicted running time difference and the predicted damage periodinformation for providing running services value and actual damage cycleinformation.

In operation 730, sending the target system normal running timeinformation to the actual running time difference value and the actualdamage period information, so as to manage the system actual runningtime according to the target system normal running time information.

It can be understood that, when executing the content described in theabove-mentioned operation 710 to operation 730, the target system uptimeinformation is received, and the target system uptime informationcarries the predicted running time difference and predictioncorresponding to the historical running time information and damagecycle information; extracting time features according to the predictedrunning time difference and predicted damage cycle information, anddetermining the actual running time difference and actual damage cycleinformation for providing operation services corresponding to thepredicted running time difference and predicted damage cycleinformation; the target system uptime information is sent to the actualrunning time difference and the actual damage period information, so asto manage the actual system running time according to the target systemuptime information. The life cycle of the system is predicted with thesame method as above, so that it can be replaced in a non-working state,which effectively saves time and reduces costs.

Based on above-mentioned foundation, before receiving target systemuptime information, described method further comprises the contentdescribed in following operation A1-operation A5:

In operation A1, receiving the coefficient corresponding to the actualrunning time of the system; wherein, the coefficient value of the actualrunning time of the system is carried in the coefficient correspondingto the actual running time of the system.

In operation A2, according to described coefficient value, assigndescribed predicted running time difference value and describedpredicted damage period information to described system actual runningtime.

In operation A3, according to described coefficient value, determinedescribed actual running time difference value and described actualdamage cycle information.

In operation A4, adding the mapping relationship between the predictedrunning time difference, the predicted damage period information and theactual running time difference, the actual damage period informationinto the time feature.

In operation A5, according to described coefficient value and describedactual operation time difference value, described actual damage cycleinformation distribution described system actual running time and systemactual running time mark.

It can be understood that, when executing the described content ofabove-mentioned operation A1-operationA5, before receiving the targetsystem uptime information, the coefficient is effectively monitored, sothat the received target system uptime information may be guaranteedprevious accuracy.

Based on above-mentioned foundation, also include the content describedin following operation A21-operationA26:

In operation A21, according to the coefficient value, assign thehistorical running time average value corresponding to the predictedrunning time difference value to the actual running time of the system.

In operation A22, wherein the determining the actual running timedifference value and the actual damage period information according tothe coefficient value includes:

In operation A23, for each actual damage period information, determinean actual running time difference and an actual damage periodinformation according to the coefficient value.

In operation A24, wherein the mapping relationship between the predictedrunning time difference, the predicted damage period information, theactual running time difference, and the actual damage period informationis added to the time features include:

In operation A25, adding the mapping relationship between the predicteddamage period information and the historical running time average valueinto the time feature.

In operation A26, adding the mapping relationship between the predictedrunning time difference, each actual damage period information, theactual running time difference corresponding to each actual damageperiod information, and the actual damage period information to the timefeature.

It can be understood that when executing the content described in theabove-mentioned operation A21-operationA26, the historical record isqueried, so that the life cycle can be more accurately judged accordingto the historical record.

In the actual operation process, the inventor found that when the timefeature was extracted according to the predicted running time differencevalue and the predicted damage period information, there was a problemof feature extraction error, so that it was difficult to accuratelydetermine the predicted running time. The actual running time differenceand the actual damage period information used to provide the operationservice corresponding to the time difference value and the predicteddamage period information. Extracting time features from the predicteddamage period information, and determining the actual running timedifference value and actual damage period information corresponding tothe predicted running time difference value and the predicted damageperiod information for providing the operation service, the specificoperations may include the following operations as described inoperation A261 and operation A262.

In operation A261, according to the mapping relationship between thepredicted damage period information and the historical running timeaverage value, determining the historical running time average valuecorresponding to the predicted damage period information.

In operation A262, according to the historical running time averagevalue, extracting time feature respectively, obtaining the actualrunning time difference and the actual damage period informationcorresponding to each actual damage period information.

It can be understood that, when executing the content described in theabove-mentioned in operations A261 and A262, when the time feature isextracted according to the predicted running time difference and thepredicted damage period information, the problem of feature extractionerrors is avoided, therefore, user can accurately determine the actualrunning time difference and actual damage period information forproviding the operation service corresponding to the predicted runningtime difference and the predicted damage period information.

In the actual operation process, the inventor found that when the firstrunning time calculation model was also carried in the coefficientcorresponding to the actual running time of the system, there was aproblem of model calculation error, so that it was difficult to reliablycalculate the coefficient value is to allocate the predicted runningtime difference value and the predicted damage period information to theactual running time of the system. In order to improve the abovetechnical problem, the coefficient corresponding to the actual runningtime of the system described in operation A2 also carries the firstrunning time calculation model, the operation of allocating thepredicted running time difference value and the predicted damage periodinformation according to the coefficient value for the actual runningtime of the system may specifically include the content described in thefollowing operations Q1-Q5.

In operation Q1, according to the coefficient value, the first runningtime calculation model to allocate the predicted running time differencevalue and the predicted damage period information for the actual runningtime of the system.

In operation Q2, determining the actual running time difference valueand the actual damage period information according to the coefficientvalue, including:

In operation Q3, according to the coefficient value, the first runningtime calculation model to determine the actual running time differencevalue and the actual damage cycle information.

In operation Q4, adding the mapping relationship between the predictedrunning time difference, the predicted damage period information and theactual running time difference, and the actual damage period informationinto the time feature include.

In operation Q5, the mapping relationship between the predicted runningtime difference, the predicted damage period information and the actualrunning time difference, the actual damage period information, and thefirst running time calculation model add to the temporal feature.

It can be understood that, when carrying out the content described inabove-mentioned operation Q1-operationQ5, when also carrying the firstrunning time calculation model in the coefficient corresponding to theactual running time of the system, the problem of model calculationerror is avoided, therefore, the predicted running time difference valueand the predicted damage period information can be reliably allocated tothe actual running time of the system according to the coefficientvalue.

Based on above-mentioned foundation, also comprise the content describedin following operations W1-W5:

In operation W1, receiving replacement system time information, anddescribed replacement system time information carries the second runningtime calculation model and system actual running time mark.

In operation W2, determining the coefficient value of the actual runningtime of the system according to the actual running time of the system.

In operation W3, according to the coefficient value and the secondrunning time calculation model, assigning the actual damage periodinformation and the actual damage parameter to the actual running timeof the system.

In operation W4, determining the replacement time error coefficientvalue corresponding to the actual running time difference valueaccording to the coefficient value and the second running timecalculation model.

In operation W5, adding the mapping relationship between the actualdamage period information, the actual damage parameter and the actualrunning time difference, the replacement time error coefficient value,and the second running time calculation model to the time feature.

It can be understood that, when carrying out the described content ofabove-mentioned operation W1-operationW5, by calculating, may carry outaccurate extraction to time feature, just may obtain the accurate timeinformation of life cycle like this.

Based on above-mentioned foundation, also comprise the content describedin following operation W21-operationW23:

In operation W21, monitoring the state of the actual system running timecorresponding to the system actual running time mark.

In operation W22, when determining that the valid period correspondingto the actual running time of the system arrives, release the actualrunning time of the system.

In operation W23, removing the mapping relationship described in thetime feature.

It can be understood that when carrying out the described content ofabove-mentioned operation W21 and operationW23, the limited equipment ofequipment may be obtained reliably, and timely replacement is carriedout, thus effectively reducing cost.

Based on above-mentioned foundation, described target system normalrunning time information also carries user identification, in accordingto described predicted running time difference value and describedpredicted damage period information extraction time feature, determiningdescribed predicted running time difference value and all Before theactual running time difference and the actual damage period informationcorresponding to the predicted damage period information for providingthe running service, the following operationsY1 and operationY2 are alsoincluded.

In operation Y1, according to described user identification, describedtarget system uptime information is authenticated.

In operation Y2, when the authentication is passed, extracting timefeatures according to the predicted running time difference and thepredicted damage period information, and determining the actual runningtime difference and actual damage period information for providing therunning service corresponding to the predicted running time differenceand the predicted damage period information.

It can be understood that, when carrying out the content described inabove-mentioned operation Y1 and operation Y2, the error range may becalculated by actual difference and predicted difference, so that theaccurate life cycle accuracy may be improved.

In the actual operation process, the inventor found that the targetsystem uptime information is a write data request, and the write datarequest further includes the data to be written. The sending the targetsystem uptime information to the actual running time difference and theactual damage period information, in order to improve the abovetechnical problem, the target system uptime information described inoperation Q2 is write data request. The write data request furtherincludes the data to be written, and the step of sending the targetsystem normal running time information to the actual running timedifference value and the actual damage period information mayspecifically include the content described in operation Q21.

In operation Q21, sending the data to be written to the actual runningtime difference value and the actual damage period information toreplace the data corresponding to the actual running time identifier ofthe system.

It can be understood that, when executing the content described inabove-mentioned operation Q21, it can be changed in time andeffectively, effectively saving time cost.

Based on the same inventive concept, also provide a comprehensivemanagement system for the life cycle of a natural gas energy meteringInternet of Things system, including: a user platform, a serviceplatform, a management platform, a sensor network platform and aperception control platform, the user platform It is communicativelyconnected to the service platform, the service platform iscommunicatively connected to the management platform, the managementplatform is communicatively connected to the sensor network platform,the sensor network platform is communicatively connected to theperception control platform, and the management platform furtherincludes a data obtaining terminal and a data processing terminal, thedata obtaining terminal and the data processing terminal are connectedin communication, and the data processing terminal is specifically usedfor:

-   -   Receiving target system uptime information, the target system        uptime information carrying the predicted running time        difference and predicted damage period information corresponding        to the historical running time information;    -   Extracting time features according to the predicted running time        difference and the predicted damage period information, and        determining the actual running time difference and the actual        running time difference corresponding to the predicted running        time difference and the predicted damage period information for        providing running services damage cycle Information;    -   Sending the target system uptime information to the actual        uptime difference value and the actual damage period        information, to manage the system actual uptime according to the        target system uptime information.

FIG. 8 illustrates a module diagram of a device for managing a pipenetwork of natural gas according to some embodiments of the presentdisclosure.

Receiving module 810 is configured to receive target system uptimeinformation, and the target system uptime information carries thepredicted running time difference and predicted damage periodinformation corresponding to the historical running time information;

The feature obtaining module 820 is configured to extract time featuresaccording to the predicted running time difference and the predicteddamage period information, and determining the actual running timedifference and actual damage period information for providing theoperation service corresponding to the predicted running time differenceand the predicted damage period information;

The managing module 830 is configured to send the target system normalrunning time information to the actual running time difference value andthe actual damage period information, so as to manage the system actualrunning time according to the target system normal running timeinformation.

A comprehensive management system for the life cycle of a natural gasenergy metering Internet of Things method and system provided in someembodiments of the present disclosure, by receiving target system uptimeinformation, the target system uptime information carries the predictedrunning time difference corresponding to the historical running timeinformation and predicted damage period information; extracting the timefeature according to the predicted running time difference and thepredicted damage period information, and determining the actual runningtime difference and actual damage period information corresponding tothe predicted running time difference and the predicted damage periodinformation for providing operation services; sending the target systemuptime information to the actual uptime difference and actual damageperiod information to manage the actual system uptime based on thetarget system uptime information. The system life cycle may be predictedby the above method, and maintenance may be carried out in thenon-working state, which effectively saves time and reduces costs.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment,” “one embodiment,” or “an alternativeembodiment” in various portions of the present disclosure are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Additionally, the order in which elements and sequences of the processesdescribed herein are processed, the use of alphanumeric characters, orthe use of other designations, is not intended to limit the order of theprocesses and methods described herein, unless explicitly claimed. Whilevarious presently contemplated embodiments of the invention have beendiscussed in the foregoing disclosure by way of example, it is to beunderstood that such detail is solely for that purpose and that theappended claims are not limited to the disclosed embodiments, but, onthe contrary, are intended to cover all modifications and equivalentarrangements that are within the spirit and scope of the embodimentsherein. For example, although the system components described above maybe implemented by hardware devices, they may also be implemented bysoftware-only solutions, such as installing the described system on anexisting server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various embodiments. This method ofdisclosure, however, is not to be interpreted as reflecting an intentionthat the claimed subject matter requires more features than areexpressly recited in each claim. Rather, claimed subject matter may liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities or propertiesused to describe and claim certain embodiments of the present disclosureare to be understood as being modified in some instances by the term“about,” “approximate,” or “substantially.” For example, “about,”“approximate,” or “substantially” may indicate ±20% variation of thevalue it describes, unless otherwise stated. Accordingly, in someembodiments, the numerical parameters set forth in the writtendescription and attached claims are approximations that may varydepending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the present disclosure are approximations, thenumerical values set forth in the specific examples are reported asprecisely as practicable.

For each patent, patent present disclosure, patent present disclosurepublications and other materials referenced in the present disclosure,such as articles, books, instructions, publications, documents, etc.,here, all of them will be incorporated herein by reference. Except forthe present disclosure history documentation of the present disclosureor the conflict, there is also an except for documents (current or afterthe present disclosure), which are available in the present disclosure.

Finally, it should be understood that the embodiments described in thepresent disclosure are intended to illustrate the principles of theembodiments of the present disclosure. Other deformations may alsobelong to the scope of the present disclosure. Thus, as an example, notlimited, the alternative configuration of the present disclosureembodiment can be consistent with the teachings of the presentdisclosure. Accordingly, the embodiments of the present disclosure arenot limited to the embodiments of the present disclosure clearlydescribed and described.

What is claimed is:
 1. A method for determining a maintenance time of apipe network of natural gas, implemented by a processor, the methodcomprising: obtaining pipe network information of natural gas in atleast one area, the pipe network information including a running time ofa system for determining a maintenance time of a pipe network of naturalgas and gas leakage information of the pipe network; extracting featureinformation based on the running time and the gas leakage information;generating a pipe network maintenance value through a maintenance valueprediction model based on pipe network maintenance information and pipenetwork environment information, the pipe network maintenance valuereflecting a priority of pipe network maintenance processing; whereinthe maintenance value prediction model is a Graph Neural Network model,a plurality of nodes of the Graph Neural Network model include aplurality of historical maintenance locations of the pipe network andhistorical pipe network environmental information, a plurality of edgesof the Graph Neural Network model include one or more pipes between theplurality of historical maintenance locations of the pipe network,features of the nodes include at least one of replacement pipe material,a maintenance time, a maintenance location, gas leakage aftermaintenance, a vibration detection result, a vibration frequency of thepipe network, and natural gas usage environment information, andfeatures of the edges include at least one of pipe material, a diameter,a connection manner, and a relationship between the historical pipenetwork environment information and the historical maintenance locationsof the pipe network; and predicting the maintenance time of the pipenetwork based on the feature information and the pipe networkmaintenance value using a maintenance time prediction model, themaintenance time prediction model being a machine learning model.
 2. Themethod of claim 1, wherein the pipe network environment informationcomprises at least one of the vibration frequency of the pipe network orthe natural gas usage environment information; and the pipe networkmaintenance information comprises at least one of the replacement pipematerial, the maintenance time, the maintenance location, the gasleakage after maintenance, or the vibration detection result.
 3. Themethod of claim 2, wherein the vibration frequency of the pipe networkincludes a natural frequency of a pipe and an external vibrationfrequency, the natural frequency of the pipe is a frequency of vibrationgenerated due to changes in an elbow or a diameter of the pipe, or dueto a flow of the natural gas, and the external vibration frequency is afrequency of vibration caused by a surrounding construction site,traffic, or an unstable pipe support.
 4. The method of claim 1, whereinan input of the maintenance time prediction model further comprises avibration fatigue factor of the pipe network; and the vibration fatiguefactor of the pipe network is calculated based on the vibrationfrequency of the pipe network and a vibration time of the pipe network.5. The method of claim 4, wherein the vibration fatigue factor of thepipe network is determined by processing the vibration frequency of thepipe network, the vibration time of the pipe network, and pipe materialstrength using a second model, the vibration fatigue factor of the pipenetwork reflects strength of pipe fatigue due to vibration, and thesecond model is a machine learning model.
 6. The method of claim 5,wherein the second model is obtained by training based on a historicalvibration frequency of the pipe network, a historical vibration time ofthe pipe network, and historical pipe material strength.
 7. The methodof claim 1, wherein the maintenance value prediction model is obtainedby training based on training data, the training data includes featureinformation corresponding to a historical running time and historicalgas leakage information, a historical pipe network maintenance value,and a historical vibration fatigue factor of the pipe network; thehistorical vibration fatigue factor of the pipe network is determinedbased on a historical vibration frequency of the pipe network and ahistorical vibration time of the pipe network, and a label of thetraining data is a maintenance time of the pipe network corresponding tothe training data.
 8. A system for determining a maintenance time of apipe network of natural gas, comprising: an information obtaining moduleconfigured to obtain pipe network information of natural gas in at leastone area, the pipe network information including a running time of thesystem and gas leakage information of the pipe network; a featureextracting module configured to extract feature information based on therunning time and the gas leakage information; a time prediction moduleconfigured to: generate a pipe network maintenance value through amaintenance value prediction model based on pipe network maintenanceinformation and pipe network environment information, the pipe networkmaintenance value reflecting a priority of pipe network maintenanceprocessing; wherein the maintenance value prediction model is a GraphNeural Network model, a plurality of nodes of the Graph Neural Networkmodel include a plurality of historical maintenance locations of thepipe network and historical pipe network environmental information, aplurality of edges of the Graph Neural Network model include one or morepipes between the plurality of historical maintenance locations of thepipe network, features of the nodes include at least one of replacementpipe material, a maintenance time, a maintenance location, gas leakageafter maintenance, a vibration detection result, a vibration frequencyof the pipe network, and natural gas usage environment information, andfeatures of the edges include at least one of pipe material, a diameter,a connection manner, and a relationship between the historical pipenetwork environment information and the historical maintenance locationsof the pipe network; and predict the maintenance time of the pipenetwork based on the feature information and the pipe networkmaintenance value using a maintenance time prediction model, themaintenance time prediction model being a machine learning model.
 9. Thesystem of claim 8, wherein the pipe network environment informationcomprises at least one of the vibration frequency of the pipe network orthe natural gas usage environment information; and the pipe networkmaintenance information comprises at least one of the replacement pipematerial, the maintenance time, the maintenance location, the gasleakage after maintenance, or the vibration detection result.
 10. Thesystem of claim 9, wherein the vibration frequency of the pipe networkincludes a natural frequency of a pipe and an external vibrationfrequency, the natural frequency of the pipe is a frequency of vibrationgenerated due to changes in an elbow or a diameter of the pipe, or dueto a flow of the natural gas, and the external vibration frequency is afrequency of vibration caused by a surrounding construction site,traffic, or an unstable pipe support.
 11. The system of claim 8, whereinan input of the maintenance time prediction model further comprises avibration fatigue factor of the pipe network; and the vibration fatiguefactor of the pipe network is calculated based on the vibrationfrequency of the pipe network and a vibration time of the pipe network.12. The system of claim 11, wherein the vibration fatigue factor of thepipe network is determined by processing the vibration frequency of thepipe network, the vibration time of the pipe network, and pipe materialstrength using a second model, the vibration fatigue factor of the pipenetwork reflects strength of pipe fatigue due to vibration, and thesecond model is a machine learning model.
 13. The system of claim 12,wherein the second model is obtained by training based on a historicalvibration frequency of the pipe network, a historical vibration time ofthe pipe network, and historical pipe material strength.
 14. The systemof claim 8, wherein the maintenance value prediction model is obtainedby training based on training data, the training data includes featureinformation corresponding to a historical running time and historicalgas leakage information, a historical pipe network maintenance value,and a historical vibration fatigue factor of the pipe network; thehistorical vibration fatigue factor of the pipe network is determinedbased on a historical vibration frequency of the pipe network and ahistorical vibration time of the pipe network, and a label of thetraining data is a maintenance time of the pipe network corresponding tothe training data.
 15. A non-transitory computer readable medium storinginstructions, when executed by at least one processor, causing the atleast one processor to implement a method comprising: obtaining pipenetwork information of natural gas in at least one area, the pipenetwork information including a running time of a system for determininga maintenance time of a pipe network of natural gas and gas leakageinformation of the pipe network; extracting feature information based onthe running time and the gas leakage information; generating a pipenetwork maintenance value through a maintenance value prediction modelbased on pipe network maintenance information and pipe networkenvironment information, the pipe network maintenance value reflecting apriority of pipe network maintenance processing; wherein the maintenancevalue prediction model is a Graph Neural Network model, a plurality ofnodes of the Graph Neural Network model include a plurality ofhistorical maintenance locations of the pipe network and historical pipenetwork environmental information, a plurality of edges of the GraphNeural Network model include one or more pipes between the plurality ofhistorical maintenance locations of the pipe network, features of thenodes include at least one of replacement pipe material, a maintenancetime, a maintenance location, gas leakage after maintenance, a vibrationdetection result, a vibration frequency of the pipe network, and naturalgas usage environment information, and features of the edges include atleast one of pipe material, a diameter, a connection manner, and arelationship between the historical pipe network environment informationand the historical maintenance locations of the pipe network; andpredicting the maintenance time of the pipe network based on the featureinformation and the pipe network maintenance value using a maintenancetime prediction model, the maintenance time prediction model being amachine learning model.